TSMDA: Target and symptom-based computational model for miRNA-disease-association prediction.

ABSTRACT: The emergence of high-throughput sequencing techniques has revealed a primary role of microRNAs (miRNAs) in a wide range of diseases, including cancers and neurodegenerative disorders. Understanding novel relationships between miRNAs and diseases can potentially unveil complex pathogenesis mechanisms, leading to effective diagnosis and treatment. The investigation of novel miRNA-disease associations, however, is currently costly and time consuming. Over the years, several computational models have been proposed to prioritize potential miRNA-disease associations, but with limited usability or predictive capability. In order to fill this gap, we introduce TSMDA, a novel machine-learning method that leverages target and symptom information and negative sample selection to predict miRNA-diseas

SUBMITTER: Uthayopas K 

PROVIDER: S-EPMC8479276 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature